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Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?

Author

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  • Maryam Dilmaghani

Abstract

Purpose - The purpose of this paper is to use data mined from Google Trends, in order to predict the unemployment rate prevailing among Canadians between 25 and 44 years of age. Design/methodology/approach - Based on a theoretical framework, this study argues that the intensity of online leisure activities is likely to improve the predictive power of unemployment forecasting models. Findings - Mining the corresponding data from Google Trends, the analysis indicates that prediction models including variables which reflect online leisure activities outperform those solely based on the intensity of online job search. The paper also outlines the most propitious ways of mining data from Google Trends. The implications for research and policy are discussed. Originality/value - This paper, for the first time, augments the forecasting models with data on the intensity of online leisure activities, in order to predict the Canadian unemployment rate.

Suggested Citation

  • Maryam Dilmaghani, 2019. "Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(2), pages 422-445, March.
    • Handle: RePEc:eme:jespps:jes-11-2017-0346
    DOI: 10.1108/JES-11-2017-0346
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    Citations

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    Cited by:

    1. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    2. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
    3. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
    4. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving: Evidence from SMS Donations," CAGE Online Working Paper Series 536, Competitive Advantage in the Global Economy (CAGE).
    5. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    6. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.

    More about this item

    Keywords

    Canada; Unemployment; Leisure; Time-use; Google Trends data; J20; J21; J22;
    All these keywords.

    JEL classification:

    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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